The Greggs-Pret Index: a Machine Learning analysis of consumer habits as a metric for the socio-economic North-South divide in England
Robin Smith, Kristian C. Z. Haverson

TL;DR
This study employs machine learning models to analyze the geographical distribution of retail outlets like Greggs and Pret in England, providing a quantitative measure of the North-South socio-economic divide and comparing it to income and other socio-economic indicators.
Contribution
It introduces a novel approach using Support Vector Machine and Neural Network regression to quantify the North-South divide based on retail location data, aligning with traditional socio-economic lines.
Findings
The Greggs-Pret dividing line closely matches income-based divisions.
The analysis confirms the M1 Watford Gap as a key North-South boundary.
Retail distribution patterns reflect broader socio-economic disparities.
Abstract
In England, it is anecdotally remarked that the number of Greggs bakeries to be found in a town is a reliable measure of the area's 'Northern-ness'. Conversely, a commercial competitor to Greggs in the baked goods and sandwiches market, Pret-a-Manger, is reputed to be popular in more 'southern' areas of England. Using a Support Vector Machine and an Artificial Neural Network (ANN) Regression Model, the relative geographical distributions of Greggs and Pret have been utilised for the first time to quantify the North-South divide in England. The calculated dividing lines were each compared to another line, based on Gross Domestic Household Income (GDHI). The lines match remarkably well, and we conclude that this is likely because much of England's wealth is concentrated in London, as are most of England's Pret-a-Manger shops. Further studies were conducted based on the relative…
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Taxonomy
TopicsMigration, Ethnicity, and Economy · Cultural Industries and Urban Development · Social and Cultural Dynamics
